Exploring the Functional Landscape of the p53 Regulatory Domain: The Stabilizing Role of Post-Translational Modifications

This study focuses on the intrinsically disordered regulatory domain of p53 and the impact of post-translational modifications. Through fully atomistic explicit water molecular dynamics simulations, we show the wealth of information and detailed understanding that can be obtained by varying the number of phosphorylated amino acids and implementing a restriction in the conformational entropy of the N-termini of that intrinsically disordered region. The take-home message for the reader is to achieve a detailed understanding of the impact of phosphorylation with respect to (1) the conformational dynamics and flexibility, (2) structural effects, (3) protein interactivity, and (4) energy landscapes and conformational ensembles. Although our model system is the regulatory domain p53 of the tumor suppressor protein p53, this study contributes to understanding the general effects of intrinsically disordered phosphorylated proteins and the impact of phosphorylated groups, more specifically, how minor changes in the primary sequence can affect the properties mentioned above.

Table S1: EE DIST , R G , and total solvent-accessible surface area (SASA) distributions computed for their full-width half maxima (FWHM), skew, kurtosis, and respective variance at different degrees of phosphorylation.(NP, SP, and FP) in the dataset, along with the mean and standard deviation of the radius of gyration (R G ), end-to-end distances (EE DIST ), and total solvent accessible surface area (SASA).The results are sorted in descending order by the percentage of each trajectory, and the overall globular trends are shown at the bottom.

Figure S1 :
Figure S1: Form factor (a) and dimensionless Kratky plot (b) of the different levels of phosphorylation on the REG of p53.

Figure S2 :
Figure S2: The root-mean-squared deviation (RMSD) for each of the trajectories projected onto a kernel-density estimation (KDE) plot to show the variability in structures upon different levels of phosphorylation.

Figure S3 :
Figure S3: The radius of gyration (R G ) for each of the trajectories projected onto a kerneldensity estimation (KDE) plot to show the variability in structures upon different levels of phosphorylation.

Figure S4 :
Figure S4: The end-to-end distance (EE DIST ) for each of the trajectories projected onto a kernel-density estimation (KDE) plot to show the variability in structures upon different levels of phosphorylation.

Figure S5 :
Figure S5: The R G (nm), EE dist , and total SASA for each trajectory in the REG of p53 shown as kernel-density estimations (KDE) plots as well as the locked counterpart in the non-phosphorylated trajectory (dashed), with vertical lines indicating the mean.

Figure S6 :
Figure S6: Total SASA per residue in the non-phosphorylated (a), and changes in the single phosphorylated (b), and fully phosphorylated (c) trajectories.

Figure S7 :
Figure S7: Total SASA variance per residue in the non-phosphorylated (a), single phosphorylated (b), and fully phosphorylated (c) trajectories, with specific residues labelled for focus.Replace Dp53 with REG.

Figure S8 :
Figure S8: Instances of secondary structure in the regulatory domain (REG) in the nonphosphorylated, REG NP , non-phosphorylated restrained, REG lock NP , non-phosphorylated high concentration, REG conc.NP , single-phosphorylated, REG SP , and fully-phosphorylated, REG FP , trajectories.

Figure S15 :
Figure S15: Ramachandran plots in residue ASP 391 for REG NP (left), REG SP (center), and REG FP (right) with specific clustered regions and the percentage of the trajectory that exists in each region.

Figure S16 :
Figure S16: Ramachandran plots in residue ARG 363 for REG NP (left), REG SP (center), and REG FP (right) with specific clustered regions and the percentage of the trajectory that exists in each region.

Figure S17 :
Figure S17: Ramachandran plots in residue LYS 373 for REG NP (left), REG SP (center), and REG FP (right) with specific clustered regions and the percentage of the trajectory that exists in each region.

Figure S18 :
Figure S18: Ramachandran plots in residue HIS 380 for REG NP (left), REG SP (center), and REG FP (right) with specific clustered regions and the percentage of the trajectory that exists in each region.

Figure S19 :
Figure S19: Ramachandran plots in residue LEU 383 for REG NP (left), REG SP (center), and REG FP (right) with specific clustered regions and the percentage of the trajectory that exists in each region.

Figure S20 :
Figure S20: Ramachandran plots in residue THR 387 for REG NP (left), REG SP (center), and REG FP (right) with specific clustered regions and the percentage of the trajectory that exists in each region.

Figure S21 :
Figure S21: Ramachandran plots in residue LYS 381 for REG NP (left), REG SP (center), and REG FP (right) with specific clustered regions and the percentage of the trajectory that exists in each region.

Figure S22 :
Figure S22: Ramachandran plots in residue PHE 385 for REG NP (left), REG SP (center), and REG FP (right) with specific clustered regions and the percentage of the trajectory that exists in each region.

Figure S23 :
Figure S23: Ramachandran plots in residue GLU 388 for REG NP (left), REG SP (center), and REG FP (right) with specific clustered regions and the percentage of the trajectory that exists in each region.

Figure S24 :
Figure S24: Ramachandran plots in residue LYS 386 for REG NP (left), REG SP (center), and REG FP (right) with specific clustered regions and the percentage of the trajectory that exists in each region.

Figure S25 :
Figure S25: Ramachandran plots in residue THR 377 for REG NP (left), REG SP (center), and REG FP (right) with specific clustered regions and the percentage of the trajectory that exists in each region.

Figure S26 :
Figure S26: Ramachandran plots in residue SER 378 for REG NP (left), REG SP (center), and REG FP (right) with specific clustered regions and the percentage of the trajectory that exists in each region.

Figure S27 :
Figure S27: Bivariate density plot representing the distribution of the radius of gyration and as a function of the distance between residues S 392 and R 379 , with representative box plots showing the influence on the radius gyration in ensembles in which they are bonded (top), ENS B and non-bonded (bottom), ENS NB for the trajectories non-phosphorylated (blue), single phosphorylated (purple) and fully phosphorylated (red).

Figure S28 :
Figure S28: Free energy plots were generated using time-lagged independent component analysis, tICA, dimensionality reduction on ϕ and ψ angles (a-d) in REG NP (a), REG locked NP (b), REG SP (c), and REG FP (d) with centers identified by agglomerative hierarchical clustering and the Silhouette score (blue) and Davies Bouldin index (red) displayed as a function of cluster size (e-h), as well as the cluster distributions for the maximum Silhouette score clustering (i-l) and the minimum Davies Bouldin index (m-p).

Figure S29 :
Figure S29: Free energy plots were generated using tICA dimensionality reduction on ϕ and ψ angles (a-d) in REG NP (a), REG locked NP (b), REG SP (c), and REG FP (d) with centers identified by agglomerative hierarchical clustering and the Silhouette score (blue) and Davies Bouldin index (red) displayed as a function of cluster size (e-h), as well as the cluster distributions for the maximum Silhouette score clustering (i-l) and the minimum Davies Bouldin index (m-p).

Figure S30 :
Figure S30: Comparison of the chemical shifts computed by Sparta+ from the nonphosphorylated trajectory to experimental values, as well as the r 2 values for each, with the restrained trajectory in ().

Figure
Figure S31: (a) Correlation plot between the phosphorylated and non-phosphorylated predicted chemical shifts as well as (d) a barchart of the chemical shifts from the nonphosphorylated trajectory and the change in chemical shifts in the (c) single-phosphorylated trajectory and the (d) fully-phosphorylated trajectory.

Table S2 :
P-values computed between each observed radius of gyration (R G ), end-to-end distances (EE dist ) and solvent-accessible surface area (SASA) in the non-phosphorylated (REG NP ), single-phosphorylated (REG SP ), and fully-phosphorylated (REG FP ) trajectories.

Table S3 :
Total SASA distributions computed for their FWHM, skew, kurtosis, and respective variance at different phosphorylation degrees, including the phosphorylated residue.

Table S4 :
Comparison of cluster globular properties of the protein REG NP .The table presents the percentage of each cluster in each trajectory of REG 351−393

Table S5 :
Comparison of cluster globular properties of REG 351−393 .The table presents the percentage of each cluster in each trajectory of REG 351−393(NP, SP, and FP)in the dataset, along with the mean and standard deviation of the radius of gyration (R G ), end-to-end distances (EE DIST ), and total solvent accessible surface area (SASA).The results are sorted in descending order by the percentage of each trajectory, and the overall globular trends are shown at the bottom.